#tumour regression

[biology-geology-beaches-india]

TSRNOSS, p 598.

[thoughtlessarse]

Researchers have discovered a link between severe COVID-19 infections and cancer tumor regression, potentially opening new avenues for cancer treatment. A study conducted by researchers from the Northwestern Medicine Canning Thoracic Institute found that RNA from the SARS-CoV-2 virus may trigger the development of immune cells with anti-cancer properties. These cells with cancer-fighting properties called inducible nonclassical monocytes (I-NCMs) could be potentially used in cancer treatments, particularly for patients with aggressive or advanced cancers, when the traditional treatment options such as immunotherapies do not work. The findings, based on studies of both human tissues and animal models, explain previous observations of reduced tumor sizes and regression of certain cancers following COVID-19 infection. "This discovery opens up a new avenue for cancer treatment. We found that the same cells activated by severe COVID-19 could be induced with a drug to fight cancer, and we specifically saw a response with melanoma, lung, breast and colon cancer in the study. While this is still in the early stages and the effectiveness was only studied in preclinical animal models, it offers hope that we might be able to use this approach to benefit patients with advanced cancers that have not responded to other treatments," said Dr. Ankit Bharat, senior author of the study.

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[immeasurablesaladagere]

Hello salad 🫶 Can I please get some little Taub or Park headcanons please? My underrated kings 🙏

I'll do both!

Taub

Is a middle regressor with an age range of 12 to around 16.

In his ✨💀emo phase💀✨ almost all the time when he's regressed. If he has a say in what he's wearing than it's all black with band T-shirts, skull accessories, too many bracelets, and beanies. He might even get into dark eye makeup if he's feeling fancy that day.

His teenager brain really hates being bald. Almost never without a hat.

Actually becomes really good at using liquid eyeliner when he's regressed, and that skill doesn't transfer to when he's big somehow.

Very "stereotypical 80's movie teen". Kind of has an apathetic "ugh, whatever" attitude about most things and doesn't like being told what to do, lots of demand avoidance.

He is willing to help caregivers out with babysitting though. He pretends he doesn't care but he does and everyone knows it.

"Taub, turn the music on your iPod down, you'll hurt your ears!" - Wilson, yelling

"Yeah whatever, Mom!" - Taub, yelling back (he turns it down secretly)

Stops liking coffee and switches to sugary energy drinks for his caffeine intake instead. He also consumes much more caffeine while regressed if no one stops him.

Most of his emotional woes are being chronically unhappy with his life choices and having a simultaneous quarter and mid-life crisis at the same time.

Touchy about his appearance, as all self-conscious teens.

Likes playing Mario and Legend of Zelda games.

Doesn't have stuffed animals, but he does have a lot of game/band keychains.

Owns a few fidgets like a fidget cube and a Rubik's cube, and has a little collection of magnets to mess with.

Journals (He insists it is not a diary)

-

Park

Little age anywhere from 2-8. She's usually all the way on one end or the other, but occasionally goes in-between.

Didn't know what the heck was going on with her when she first regressed starting in med-school because of the stress, and of course, being a med-student, she jumped to the worst possible conclusion and thought she had a brain tumour or something.

She just nervously sat on that information until her psych class discussed age regression, and then just went "Oh."

Regresses almost entirely involuntarily and due to stress or fear (she also regressed that one time she was on acid, only House really picked up on it). It doesn't happen very often, usually when she's sleep deprived and otherwise upset.

Has a box of the bare-minimum in the way of little gear. A pacifier that's light blue, a colouring book with some crayons, a white bear with a pink bow around its neck, and a little whiteboard list of self-care things so she remembers to actually do them.

Refers to her caregivers very respectfully, using Mr. and Mrs. for everything, or in the case of her coworkers, Doctor [name]. She will not stop calling them that even though they've said she doesn't have to.

Asks people bluntly if they can shut up so she can read her picture books.

The kind of kid where if you sit them down at those restaurants with the paper on the tables for colouring she'll do her best to make a masterpiece and impress the workers.

Will ask you why you look so ugly. She's not being mean, she genuinely wants to know. Absolutely no filter.

Rambles about her current interests and will just keep going if you don't stop her.

Her favourite caregiver is Cuddy, she likes to give her the pictures she draws.

Watches nature documentaries about the ocean and unironically watches the Telletubbies. She loves how creepy they look.

[decentralvaccine]

The Promising Emergence Of Cancer Vaccines

Towards the end of the 19th century William Coley, a surgeon in New York, made a surprising observation. One of his patients, close to death with a neck tumour, recovered after catching a serious bacterial skin infection. Intrigued, Coley tried to replicate the finding, injecting patients with a cocktail of killed bacteria to get their cancers to regress. He ended up treating over a thousand patients in this way, often successfully.

[moko1590m]

2024年11月27日 12時00分 重度の新型コロナ発症によってがん腫瘍が縮小するという研究結果、新たながん治療法の開発につながる可能性も アメリカ・ノースウェスタン大学の研究チームが発表した論文で、「重度の新型コロナウイルス感染症(COVID-19)によってがん腫瘍が縮小する可能性がある」という結果が示されました。この新しい発見により、がん治療の新たな可能性が開かれるのではないかと期待が寄せられています。 JCI - Inducible CCR2+ nonclassical monocytes mediate the regression of cancer metastasis https://www.jci.org/articles/view/179527 COVID caused cancer tumours to shrink in mice – new study https://theconversation.com/covid-caused-cancer-tumours-to-shrink-in-mice-new-study-243973 がんの治療法には腫瘍を直接切除する外科手術や、がん細胞の増殖を抑えて死滅させる抗がん剤治療、がん細胞の遺伝子を傷つける放射線療法、免疫系を活性化させてがん細胞を攻撃させるがん免疫療法などがあります。中でもがん免疫療法は近年注目を集めており、さまざまな研究結果が蓄積されています。 し��し、がん免疫療法の効果がある症例は20～40％程度にとどまっており、すべての患者に効果があるわけではありません。既存のがん免疫療法は主にT細胞と呼ばれる免疫細胞を対象にしたもので、体内で十分な機能を持つT細胞を産生できない時にがん免疫療法が失敗するケースが多いことから、T細胞への依存はがん免疫療法の効果を制限する要因と考えられているとのこと。 一方、COVID-19などのウイルス感染症にかかったヒトや、特定のワクチンを接種したヒトでは免疫システムが活性化され、その他の感染症や疾患に対しても抵抗性を示すようになることが知られています。これはtrained immunity(訓練免疫)と呼ばれるもので、さまざまな疾患を治療する新たなアプローチにつながる可能性を秘めています。 ノースウェスタン大学の研究チームは、重度のCOVID-19によって生じる訓練免疫ががんの治療に効果を示すのかどうかを確かめるため、マウスを対象にした動物実験を行いました。実験では、ステージ4の悪性黒色腫(メラノーマ)・肺がん・乳がん・結腸がんなどを持つマウスに、重度のCOVID-19に対する免疫応答を模倣した薬物を投与し、特殊な単球を産生するように促しました。 単球は免疫細胞の一種であり、感染に対する防御機構において重要な役割を果たしています。しかし、がん腫瘍に入り込むと「腫瘍随伴マクロファージ(TAM)」となり、がん細胞の増殖や浸潤、転移を促すと共にT細胞の働きを抑制するようになります。 今回の実験では、重度のCOVID-19によって産生される特殊な単球はTAMに変換される通常の単球とは異なり、がん細胞と戦う特性を保持し続けていることが判明。特殊な単球は腫瘍に移動し、ナチュラルキラー細胞を活性化してがん細胞の攻撃を促すことが確認されました。特殊な単球の産生を促されたマウスでは、4種類のがんすべてで腫瘍が縮小し始めたと報告されています。 アングリア・ラスキン大学で生物医学教授を務めるジャスティン・ステビング氏は、「このメカニズムは、現在の多くの免疫療法が焦点を当てているT細胞に依存せずにがんと戦う新しいアプローチを提供するため、特にエキサイティングです」と述べ、従来のがん免疫療法では効果がない患者にとっての新しい選択肢になり得るという見解を示しました。 今回の研究結果はあくまでマウスでの実験に基づいたものであり、同様の効果がヒトの体内でも見られるかどうかを判断するには臨床試験が必要です。また、新型コロナウイルスワクチンは新型コロナウイルスのRNA配列の一部しか使用していないため、ワクチン接種で同様の効果が起こる可能性は低いとのこと。 それでも、特殊な単球が関わるメカニズムはがんに普遍的なものであるため、ヒトの体でも同様の効果が起こる可能性は十分にあり、新薬やワクチン���開発につながることが期待されています。 ステビング氏は、「これらの発見をヒト患者の治療につなげるには、まだ多くの課題が残されています。しかし、この研究はウイルス・免疫系・がんの間の複雑な関係の理解について、エキサイティングな一歩を踏み出しました。これは新たな治療法への希望をもたらすと同時に、科学的発見が予期せぬ形で医学的ブレイクスルーをもたらすことがあることを強調しています」と述べました。 この記事のタイトルとURLをコピーする ・関連記事 新型コロナの後遺症「ロングCOVID」に悩む患者の全身スキャンで「体中の組織でT細胞が異常に活性化」していることが明らかに - GIGAZINE 新型コロナウイルス感染後の後遺症「ロングCOVID」で免疫システムが変化してしまう可能性 - GIGAZINE すべての新型コロナウイルス変異株を防御できる可能性のある抗体「SC27」が発見される、新型コロナの万能ワクチン開発に光 - GIGAZINE 新型コロナウイルスに感染すると強力な免疫を得られる可能性があるがわざと感染すべきではない - GIGAZINE インフルエンザワクチンを接種した子どもは新型コロナウイルス感染症が重症化する可能性が低いことが判明 - GIGAZINE 「がん遺伝子」を逆手に取って不死身のがん細胞を自殺に追いやる画期的な治療法 - GIGAZINE 革新的な免疫療法が脳腫瘍との戦いに新たな希望を見出されている - GIGAZINE がん細胞の遺伝子に「自滅スイッチ」を組み込んで抗がん剤耐性の獲得を防ぎつつ治療する試み - GIGAZINE ・関連コンテンツ 「普通の風邪にかかった時の記憶」が新型コロナウイルス感染症にも有効である可能性 新型コロナウイルスの変異株にワクチンは有効なのか？ 「がん細胞だけを殺す新型の免疫細胞」をCRISPR-Cas9で開発することに成功 遺伝子操作した免疫細胞に「殺し」を教えてがん細胞を死に至らしめる「CAR-T療法」とは？ 「がんワクチン」によって全身の腫瘍が消滅することがマウスによる実験で明らかに がんの免疫療法は「がん細胞の遺伝子変異量が多いほど効果的」である可能性が示唆される 革新的な免疫療法が脳腫瘍との戦いに新たな希望を見出されている がんから人類を救うかもしれない「がんワクチン」の開発が進んでいる

重度の新型コロナ発症によってがん腫瘍が縮小するという研究結果、新たながん治療法の開発につながる可能性も - GIGAZINE

[ivy-hospital]

Can brain tumours disappear without treatment?

The idea of brain tumors disappearing without treatment might seem like something out of a medical miracle, but it's not entirely unheard of. While rare, there have been cases reported where brain tumors seemingly vanish or shrink without any medical intervention. However, before delving into these exceptional cases, it's crucial to understand the nature of brain tumors and their usual course of progression.

Brain tumors are abnormal growths of cells in the brain. They can be either benign (non-cancerous) or malignant (cancerous). Benign tumors tend to grow slowly and may not invade surrounding tissues, while malignant tumors can spread and pose a more significant threat to health. The symptoms of a brain tumor vary depending on its size, location, and rate of growth, but they often include headaches, seizures, cognitive changes, and neurological deficits.

In most cases, brain tumors require medical intervention, such as surgery, radiation therapy, or chemotherapy, to manage symptoms, slow down growth, or remove the tumor altogether. However, there have been rare instances where brain tumors appear to regress spontaneously, without any treatment.

One possible explanation for this phenomenon is the immune system's role in fighting cancer. The immune system is equipped with specialized cells and mechanisms to recognize and eliminate abnormal cells, including cancerous ones. Occasionally, the immune system may mount a robust response against a tumor, leading to its regression or disappearance. This process, known as spontaneous tumor regression, is extremely rare but has been documented in various types of cancer, including brain tumors.

Another factor that may contribute to the apparent disappearance of brain tumors is misdiagnosis or imaging artifacts. Sometimes, what appears to be a tumor on imaging tests such as MRI or CT scans may turn out to be a benign lesion, a vascular abnormality, or even a technical artifact. In such cases, the tumor-like appearance resolves on its own without any treatment because there was never a tumor present in the first place.

Furthermore, advancements in imaging technology and medical knowledge have led to more accurate diagnoses of brain tumors, reducing the likelihood of misinterpretation or overdiagnosis. However, despite these advancements, there are still cases where tumors seem to vanish without a clear explanation.

It's essential to approach reports of spontaneous tumor regression with caution and skepticism, as they are exceedingly rare and often poorly understood. While these cases offer hope and inspiration, they should not overshadow the importance of early detection, timely intervention, and ongoing medical care in managing brain tumors effectively.

In conclusion, while it's theoretically possible for brain tumors to disappear without treatment, such occurrences are exceptionally rare and often poorly understood. The mechanisms behind spontaneous tumor regression remain a subject of ongoing research and debate. Nonetheless, these cases remind us of the complexities of cancer biology and the remarkable resilience of the human body in its fight against disease.

Ivy Hospital stands out as a premier destination for brain tumor treatment across Mohali, Hoshiarpur, Khanna, Nawanshahr, and Amritsar. Renowned for its cutting-edge facilities and expert medical staff, Ivy Hospital offers comprehensive care for patients grappling with brain tumors. With state-of-the-art technology and a multidisciplinary approach, Ivy Hospital ensures precise diagnosis and tailored treatment plans, ranging from surgery to radiation therapy and chemotherapy. Their commitment to patient-centric care, coupled with a track record of successful outcomes, cements Ivy Hospital's reputation as a top choice for individuals seeking top-notch brain tumor treatment in the region.

[drmboyiinc]

Transforming Cancer Care: The Power of Immunotherapy Treatment

Immunotherapy treatment is a groundbreaking treatment option for patients suffering from various types of cancer. Immunotherapy, which uses the body's immune system to fight cancer, has altered the landscape of cancer care, providing a beacon of hope to those suffering from this devastating disease.

Understanding Immunotherapy: How It Works: Immunotherapy uses the body's natural defence system, the immune system, to identify and attack cancer cells. Unlike traditional cancer treatments like chemotherapy and radiation therapy, which directly target cancer cells, immunotherapy treatment works indirectly by boosting the immune system to fight cancer. This method can help the immune system identify and destroy cancer cells more effectively, resulting in tumour regression and better patient outcomes.

Types of Immunotherapy: From Checkpoint Inhibitors to CAR-T Cell Therapy: Several types of immunotherapy treatments have proven effective in treating various types of cancer. These drugs, for example, work by releasing the immune system's brakes, allowing it to better recognise and attack cancer cells. CAR-T cell therapy involves genetically modifying a patient's immune cells to specifically target cancer cells, resulting in a more personalised and targeted approach to treatment. Cancer vaccines, inflammatory chemicals therapy, and adoptive cell transfer are examples of additional immunotherapies.

Success Stories: Real-Life Examples of Immunotherapy's Impact: Immunotherapy's impact on cancer care cannot be overstated, with numerous success stories demonstrating its efficacy in treating various cancer types. Patients who were previously given bleak prognoses have shown remarkable responses to immunotherapy, achieving long-term remission and, in some cases, cure. These success stories demonstrate the transformative power of immunotherapy and offer hope to patients and their families facing a cancer diagnosis.

Overcoming Challenges: The Future of Immunotherapy Research: While immunotherapy has shown great promise in cancer treatment, significant challenges remain. Immunotherapy does not work for everyone, and some people may experience serious side effects. Furthermore, research is being conducted to refine and improve immunotherapy treatments, such as identifying biomarkers that can predict patient response and developing combination therapies that increase efficacy. Despite these challenges, the future of immunotherapy looks promising, with ongoing research paving the way for further advances in cancer treatment.

Expanding Access: Addressing Disparities in Immunotherapy Treatment: Access to immunotherapy treatment varies according to geographic location, socioeconomic status, and insurance coverage. Efforts to increase access to immunotherapy for all patients, regardless of background or circumstances, are critical to ensuring equitable cancer treatment. Patient assistance programmes, clinical trial participation, and health insurance advocacy all have the potential to close the gap and ensure that all patients have access to this life-saving therapy.

Conclusion

Immunotherapy has transformed cancer care, providing new hope and promising outcomes for patients battling this formidable disease. Immunotherapy, which uses the body's immune system to fight cancer, has changed the cancer treatment landscape, giving patients and their families hope. As research continues and new advances are made, the future of immunotherapy looks even brighter, with the potential for improved outcomes and a higher quality of life for cancer patients worldwide.

[leedsomics]

StableMate: a new statistical method to select stable predictors in omics data

Inferring reproducible relationships between biological variables remains a challenge in the statistical analysis of omics data. For example, methods that identify statistical associations may lack interpretability or reproducibility. The situation can be greatly improved, however, by introducing the measure of stability into the association, where small perturbations in the data do not affect the association. We developed this concept into a new statistical framework called StableMate. Given data observed in different environments or conditions, such as experimental batches or disease states, StableMate identifies predictors which are environment-agnostic or specific in predicting the response using stabilised regression.StableMate is a flexible framework that can be applied to a wide range of biological data types and questions. We applied StableMate to 1) RNA-seq data of breast cancer to discover genes and gene modules that consistently predict estrogen receptor expression across disease conditions, 2) metagenomics data to identify fecal microbial species that show persistent association with colon cancer across studies from different countries and 3) single cell RNA-seq data of glioblastoma to discern signature genes associated with development of microglia to a pro-tumour phenotype regardless of cell location in the core. StableMate is an innovative, adaptable and efficient variable selection framework that achieves a comprehensive characterisation of a biological system for a wide range of biological data types for regression and classification analyses. http://dlvr.it/Swm91F

[sandeep-health-care]

The Role of Artificial Intelligence in Early Cancer Diagnosis

Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.

1. Introduction

Early cancer diagnosis and artificial intelligence (AI) are rapidly evolving fields with important areas of convergence. In the United Kingdom, national registry data suggest that cancer stage is closely correlated with 1-year cancer mortality, with incremental declines in outcome per stage increase for some subtypes [1]. Using lung cancer as an example, 5-year survival rates following resection of stage I disease are in the range of 70–90%; however, rates overall are currently 19% for women and 13.8% for men [2]. In 2018, the proportion of patients diagnosed with early-stage (I or II) cancer in England was 44.3%, with proportions lower than 30% for lung, gastric, pancreatic, oesophageal and oropharyngeal cancers [3]. A national priority to improve early diagnosis rates to 75% by 2028 was outlined in the National Health Service (NHS) long-term plan [4]. Internationally, early diagnosis is recognised as a key priority by a number of organisations, including the World Health Organisation (WHO) and the International Alliance for Cancer Early Detection (ACED).

Many studies indicate that screening can improve early cancer detection and mortality, but even in disease groups with established screening programmes such as breast cancer, there are ongoing debates surrounding patient selection and risk–benefit trade-offs, and concerns have been raised about a perceived ‘one size fits all’ approach incongruous with the aims of personalised medicine [5,6,7]. Patient selection and risk stratification are key challenges for screening programmes. AI algorithms, which can process vast amounts of multi-modal data to identify otherwise difficult-to-detect signals, may have a role in improving this process in the near future [8,9,10]. Moreover, AI has the potential to directly facilitate cancer diagnosis by triggering investigation or referral in screened individuals according to clinical parameters, and automating clinical workflows where capacity is limited [11]. In this review, we discuss the potential applications of AI for early cancer diagnosis in symptomatic and asymptomatic patients, focussing on the types of data that can be used and the clinical areas most likely to see impacts in the near future.

2. An Overview of Artificial Intelligence in Oncology

2.1. Definitions and Model Architectures

AI is an umbrella term describing the mimicking of human intelligence by computers (Figure 1). Machine learning (ML), a subdivision of AI, refers to training computer algorithms to make predictions based on experience, and can be broadly divided into supervised (where the computer is allowed to see the outcome data) or unsupervised (no outcome data are provided) learning. Both approaches look for data patterns to allow outcome predictions, such as the presence or absence of cancer, survival rates or risk groups. When analysing unstructured clinical data, an often-utilised technique, both in oncology and more broadly, is natural language processing (NLP) [12]. NLP transforms unstructured free-text into a computer-analysable format, allowing the automation of resource-intensive tasks.

It is common practice in ML to split data into partitions, so that models are developed and optimised on training and validation subsets, but evaluated on an unseen test set to avoid over-optimism. A summary of commonly used supervised learning methods is provided in Table 1. Such methods include traditional statistical models such as logistic regression (LR) as well as novel decision tree and DL algorithms.

Deep learning (DL) is a subgroup of ML, whereby complex architectures analogous to the interconnected neurons of the human brain are constructed. Popular Python-based frameworks for deep learning include Tensorflow (Google) and PyTorch (Facebook), which provide features for model development, training and evaluation. Google also provides a free online notebook environment, Google Colaboratory, allowing cloud-based Python use and access to graphic processing units (GPUs) without local software installation.

Although a detailed description of neural network structures is beyond the scope of this article, artificial neural networks (ANNs) can be used to illustrate the overarching principles (Figure 2). As a recent example, Muhammad et al. used an ANN to predict pancreatic cancer risk using clinical parameters such as age, smoking status, alcohol use and ethnicity [18]. In their most basic form, ANNs consist of: (1) an input layer, (2) a ‘hidden layer’, consisting of multiple nodes which multiply the input by weights and add a bias value, and (3) the output layer, passing the weighted sum of hidden layer nodes to an activation function to make predictions. Deep learning simply refers to networks with more than one hidden layer.

Figure 2. Example of a single-hidden-layer ANN architecture. (1) The smoking status in pack years and lung nodule size (mm) are entered as the two input nodes. (2) In the hidden layer, each node multiplies the values from incoming neurons by a weight (shown as decimals at incoming neurons) and aggregates them. (3) The results are passed to an activation function, converting the output to a probability of cancer between 0 and 1. Multiple learning cycles are used to update the hidden layer weights to improve performance.

Many early diagnosis models have exploited convolutional neural network (CNN) architectures, which led to a revolution in computer-vision research by allowing the use of colour images as input data. While the downstream fully connected layers resemble those of an ANN, the input data are processed by a series of kernels which slide over image colour channels and extract features, such as edges and colour gradients. These inputs are then pooled and flattened before being passed to the fully connected layer. Many pre-defined CNN architectures with varying degrees of complexity are available for use, including AlexNet [20], EfficientNet [21], InceptionNet [22], ResNet [23] and DenseNet [24]. As we discuss further in this article, CNNs have a wide range of applications in radiology and digital pathology.

2.2. Data Types: Electronic Healthcare Records

A number of emerging healthcare data modalities are suitable for analysis with AI. In recent years, a global expansion in electronic healthcare record (EHR) infrastructures has occurred, enabling vast amounts of clinical data to be stored and accessed efficiently [25]. Many exciting digital collaborations are arising to facilitate early diagnosis research using EHRs, including the UK-wide DATA-CAN hub [26]. Other digital databases record outcome measures and pathway data. For example, the Digital Cancer Waiting Times Database aims to improve cancer referral pathways through user-uploaded performance metrics [27].

It is important to draw a distinction between local hospital EHR data and national public health data registries, including those utilised by multi-centre screening studies. With registries, unified database structures are being implemented for consistency across institutions. A key aim of the NHSx ‘digital transformation of screening’ programme is to ensure interoperability of systems, so that data can flow seamlessly along the entire screening pathway, including into national registry databases [28]. An example of database unification is the new U.K. cervical cancer screening management system, which will simplify 84 different databases into a single national database, and aims to streamline data entry and provide simple, cloud-based access for users [29].

Digital databases, whether local or national, are ripe for analysis with AI, which is inherently able to process large amounts of information (‘Big Data’) [30]. EHR data typically include structured, easily quantifiable data such as admission dates or blood results, and unstructured free-text such as clinical notes or diagnostic reports. The latter can be analysed using NLP approaches. An overview of NLP in oncology is provided by Yim et al. [12], and example early diagnosis uses include identifying abnormal cancer screening results [31], auditing colonoscopy or cystoscopy standards [32,33] and identifying or risk-stratifying pre-malignant lesions [34,35,36,37,38]. NLP has also been used to automate patient identification for clinical trials, reducing the burden of eligibility checks [39]. Morin and colleagues published an exciting example of how AI and NLP technology can integrate into EHR systems: their model can analyse millions of data points and perform real-time cancer prognostication based on continuous learning of routinely collected clinical data

Read More: https://www.europeanhhm.com/articles/the-role-of-artificial-intelligence-in-early-cancer-diagnosis

[underthehedge]

Sorry weren't you talking about CTVT?

YEAH SORRY THIS IS STILL GOING BUT I SWEAR I'M NEARLY DONE.

So yeah I want to wrap this up here, to return to the original paper and an overall conclusion. I'm not being helped at all by the fact I'm currently looking after my parents' dogs while they're away; they're very cute, but their scientific literacy and focus is severely lacking. Cariad in particular is woefully uneducated on the subject of best laboratory practice or even basic genomics. But they've gone back to doggy nap time now so let's do this. If you've made it this far, well done idk how you managed that.

Additional data from the authors - vaccines?

Looking at another (much more reasonable) comment reply on ResearchGate, Lusi writes:

[...]it is not an archetypal virus . It is a new onco-agent , acutely transforming, that resist current classifications. The agent could play a role in the cancer epidemic of infectious origin . The paper you are referring to, it is an old preliminary version, released in the very beginning. Very very preliminary and also a bit confusing. What we have now instead is: Ten years of research, numerous replicas in many international research centres by independent scientists. We have a bio bank of these purified onco- agents , that can be isolated and purified from cancer specimens , just as a routine. thousands of electron microscopy images , etc etc. experiments on mice completed, now testing on dogs. Cancer shrinking with vaccine.. It is a paradigm shift in cancer. If you would like to know more and discuss , please feel free to contact me. I and the rest of the team will be happy to share . My only problem , being a medical doctor and a scientist at the same time, is that I have little tolerance with Semmelweiss-like peers. I am of course enjoying this journey, taking it less seriously than Semmelweis . Science seeks truth and truth will always prevails. We need Medical evidences and not algorithms based on scores that are not applicable for new discoveries

Most of this is unpublished still so the actual evidence is lacking, though for big works? Especially stuff that's likely to overturn decades or more of research, you really need all your ducks in a very fine row before you seriously publish.

However, there's a short letter to the editor in the British Journal of Surgery titled "Shifting Paradigms in Cancer Treatment", largely a rehash of the original paper's introduction section, but with two major additions. One is the rebranding of the giant viruses to "large transforming unicellular entities" and/or "infectious oncogenic unicellular agents" (more on this later, we're nearly onto the weirder version of the paper now lads).

The second, the addition of photographs showing reduction in tumour size in a naturally infected female dog after multiple treatments with a therapeutic vaccine they developed. Which, look perhaps it works, maybe they're right there? But it's worth noting that one of the things about CTVT, as a transmissible foreign cell, is that if generally spontaneously regresses after 6 months.

The final line of her comment above though seems to be a minor theme though that I saw in the other, long and crazy comment. I can't tell if it's a reasonable call for not dismissing new unknowns based on data that may not be applicable? Or a dismissal of the fact a bunch of stats and modelling keeps inconveniently proving her wrong?

The second version of that preprint: it gets weird here

The first preprint I mentioned was titled "A transforming giant virus discovered in Canine Transmissible Venereal Tumour: Stray dogs and Tasmanian devils opening the door to a preventive cancer vaccine." It's out there but it's all like...well it's reasonable at least. Cancer causing viruses are a thing, we know vaccination can prevent some cancers, and giant viruses are novel and do appear to have some association with humans.

The second version of that paper though?

"Living infectious agents with the same organic wall assembly of Precambrian early-life fossils discovered in Canine Transmissible Venereal Tumour and human cancer: Giant viruses or living protocells? Evaluating the effects of an anti-cancer vaccine in stray dogs, while challenging the mysteries around the RNA world."

This, uh, rather stretches the initial concept, scope and definition and also what the fuck do you mean precambrian life in dog-dick cancer???

Here's the statement of significance that accompanies it, emphasis mine:

These infectious living single-cell agents establish a new family of oncogenic organisms that resist current classifications and affect humans and animals in the wild. While only a dozen of proteins compose a classic virus, these organisms are small infectious cells, but very distinct from somatic eukaryotic cells. The identification of causative unicellular organisms that start cancer in healthy subjects and the possibility to induce cancer regression with a neutralizing vaccine change some perspectives in cancer. The Precambrian features and the genetic composition suggest that these unicellular entities are infectious living RNA protocells that finally gives form to what was considered only a hypothesis drafted by the Nobel laureate Walter Gilmore: the RNA world, the origin of life and RNA protocells.

Yup, we've gone from "boy these sure are some big viruses, deffo not the well established dog cells we have all that data on" to "actually the RNA world is here and it's giving us cancer". Fuck the paradigm shift in cancer, they're now on to identifying a whole new domain of life and providing evidence of the precursors of cellular existence as we know it.

This isn't Big If True. This is Monumentally Fucking Enormous If True. This is Rewrite All Your Bio Textbooks If True.

The paper itself starts much the same to start with, including a line that still irritates me (I'll put it in the comments). But then it gets interesting. In the first version, there was vague reference to the giant viruses clustering around some small "unicellular particles" that was...not explained? Though appeared in micrographs. Honestly I kinda missed these on my first read because they just aren't really discussed. Literally, the discussion states "Therefore, using a different strategy and avoiding filtration, we isolated from CTVT a microbial infectious unicellular organism and its satellites giant viral particles" but the whole focus is on the giant viruses.

But now? Full on reinterpretation. They're not giant viruses clustering around some ~1μm mystery particles. They're the same thing and the "viruses" are actually just budding off from them. With the focus now on the larger particles, we've got a size range of 2.5μm and up to 4μm, an order of magnitude larger than the 400nm giant viruses.

The mature unicellular organism is morphologically undistinguishable from Precambrian and Early Cambrian single-cell organisms, called acritarchs

Ok fascinating. I'd never heard of an acritarch afore so I went to our good friend wikipedia to find out that uh, "The classification is a catch all term used to refer to any organic microfossils that cannot be assigned to other groups". Not particularly helpful, but most are apparently just like, assorted Eukaryotes, with the Precambrian ones apparently mostly like, algae?

Either way, the pictures used to justify it are all just like...wrinkly blobs? I can't say they look dissimilar but also I can't say it's convincing.

And now we're on to the genetics. So they filtered away the dog genome stuff and were left with what is purportedly distinct to the Mystery Organisms. And it's...mostly transposons. Including a bunch that are just like, generic vertebrate transposons that are common in mammals. The only halfway convincing part of this is that apparently these sequences are only found in the sequencing of extracted RNA and not in DNA, though at this point I'm not exactly convinced because the wording is poor.

And then we go right off the deep-end into "well because there's a bunch of RNA elements and scrungly blobs that look like some Precambrian fossils: RNA World!!!"

The discussion is a mess, and includes another passing dig at the "limitations of bioinformatics" (which I'm more and more reading as "stop proving me wrong >:( "), but the short following conclusion ends with this:

[...]This established the fundament of our current epistemiological definition of viruses as small and filterable. Mimiviruses started the epistemiology rupture and highlighted how the circumvention of filtration techniques can bring to the light completely unexpected entities. Our infectious ancestral unicellular organisms establish a new family of oncogenic entities that resist current epistemiological classifications, but need a name. As a nod to Lucy, the first Eve, we can tentatively name these unveiled RNA protocells: Onco-LUSI.

Holy grandiosity complex, batman! Onco-LUSI, fucking spare me.

CTVT and a weird niche theory I fell down the rabbit hole about - giant transforming retroviruses???

This is a story about how a single line on a wikipedia page sent me down a rabbit hole of finding one scientist's fringe theory that's juuuust plausible enough to make me question everything while almost certainly being absolute fucking bunk.

Some background

So, on parts of tumblr at least we all know about Canine Transmissible Venereal Tumour, aka The Immortal Cancer Dog. For those who don't know, it's a cancer dogs get, usually on their junk, that unlike most other cancers, isn't made up of their own cells. The cells are actually all descended from this one dog or wolf that lived like 11,000 years ago and are, arguably, all technically that one dog. A dog that became a single-celled infectious disease.

We have a wealth of genetic, histological and observational evidence for this. As in, we know it what population of canids it came from, we know it's got a weird chromosomal structure compared to normal dogs, we know it's genetically distinct from the hosts. We also know it's not the only one out there: There's a similar thing in Syrian hamsters and also the famous Tasmanian Devil Facial Tumour Disease (DFTD).

Which made me pause when I was reading something on wikipedia about the devil facial tumour and saw a line mentioning that it was now known to be caused by a giant virus, much like CTVT. Which...huh? Oh I hadn't heard that afore.

Giant viruses

Ok so giant viruses are a thing and they're fuckin cool. They're a relatively recent discovery and comparatively huge, i.e. bigger than a bunch of bacteria. They were only discovered in 1981 and we still don't know an enormous amount about them but they're big and have large genomes and because of the way viruses are they're not easy to detect unless you're specifically looking for them.

They show up under microscopy (sometimes) and you can find them with genetic probes but you gotta already be looking for them to see that really. Current research though basically says they're more common than we think, just overlooked, and there's software out there that scans through genomic data to find sequences that might indicate their presence. There's even a possibility that one group might be involved in some cases of pneumonia in humans, though I need to stress that that's extremely not confirmed right now.

The "wait, what?" moment

So I mentioned that it was a line in the wiki article for DFTD that had me going "wait, really?", the line in question was this:

A study found evidence for an infectious agent resembling a giant virus that was capable of turning heathy cells into cancer cells. It was found to be a huge retrovirus with similar viruses being found in human and canine cancer cells.

Big If True.

So of course I check the source, which was a 2020 paper by Lusi et al. titled "A transforming giant virus discovered in Canine Transmissible Venereal Tumour: Stray dogs and Tasmanian devils opening the door to a preventive cancer vaccine".

Hang on, CTVT not DFTD? This is where some alarm bells went off because uh, as mentioned at the start, we know a shit ton about CTVT. Including the fact that it's all one specific dog. Which doesn't fit at all with the idea that it's caused by a virus transforming host cells into cancer cells.

So what fucking gives? What is this research that fully overturns decades of pretty conclusive research to the contrary?

Is this another case of Dr Barbara McClintock? Who spent decades being ridiculed by the scientific community over her wild theory that was, in fact, 100% right even if it seemed to fly in the fact of all prior evidence?

Or is this a Dr Donald I. Williamson situation wherein a scientist with appropriate training is just wildly but extremely vehemently wrong?

[bpod-bpod]

Macrophage Defector

Natural killer cells are a type of immune cell that protects the body against not only invading pathogens but also cancer, providing an innate defence against these rogue cells. Some tumours, however, keep natural kill cells at bay and thereby avoid destruction. And recent research in lung tumours reveals this natural killer cell exclusion is achieved with the help of another immune cell – the macrophage. The particular culprit is a type of macrophage covered in a protein called TREM2 – an anti-inflammatory factor. Shown above is a lung tumour (green) packed with TREM2-expressing macrophages (red) that are protecting the cancer from attack. Why these macrophages switch allegiance and side with enemy is unclear, but blocking TREM2 while boosting natural killer cell activity was shown to reduce lung tumour growth in mice suggesting a similar approach might be effective in promoting tumour regression in humans too.

Written by Ruth Williams

Image from work by Matthew D. Park and Ivan Reyes-Torres, and colleagues

Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Image copyright held by the original authors

Research published in Nature Immunology, April 2023

You can also follow BPoD on Instagram, Twitter and Facebook

[adi06lena]

little love

Paring: Robin Buckley x fem reader

Word count: 301

Warning: age regression, mommy robin, nothin else

Summary: little Y/n falls asleep cuddling mommy robin

A/n: i was switching between bigs n little for dis so be happy!!! Hop ou ikes it frien!!!

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

I had asked mommy  if we could watch grey's anatomy when she got home from work and now that time has come, us on the couch with me in my purple onesie with my panda and Robin in her matching polka dot pj set. 

She held me as we watched the newest episode from greys anatomy. I release my grip from robin and look up at her with doe eyes

“Do you need something little love?”

“Cans i hav snacks mama?’

She pushes a piece of my hair behind my ear and kisses my forehead

“What snack would you like baby girl?”

“Popscorn n swawbewies pweas?”

Robin let out a little giggle before detaching herself from me 

“Alright baby ill be right back with that”

“Fank ou mommy” 

“Of course little lovely”

Robin walks off and I snuggle for the panda to get comfy till robin comes back. I can hear her behind me doing her thing.

I watch as Dr Grey performs a life saving surgery on a cancerous patient. I watch as she removes a huge tumour with the help of dr wilson and dr bailey.

Just as they finish taking out the tumour i hear mama coming back with the snacks.

“Here we go little baby, your popcorn and strawberries as requested”

I reach out to make grabby hands and she hands me my strawberries

“Tank ou mommy!”

She sits down next to me and we snuggle closer as I eat my strawberries and mommy eats my popcorn.

Mommy moves her hands to my side and starts rubbing. It makes my eyes start to droop.

“M seepy mommy”

‘Its okay lovely im here, you can go to sleep”

“Otay mama lov ou”

I snuggle more into her as i slowly fall asleep.

“I love you to my angel” 

~~~~~~~~~~~~~~~

Taglist: @itsthescarletwitch @gay-trash-in-a-paperbag @yelenabemylova @macaroni-with-hotsause

(Lmk if ou wans ta be aded 💕)

[shinakazami1]

Recently found your fernarator au and am curious: is the Stanley becoming a plant one ending or inevitable in the au?

-employee 432 anon

ANON GOSH AAAA THANK YOU FOR BEING INTERESTED IN MY AU ;W;

Ok this is a great question - no, it's not inevitable, it's somewhat of a separate thing that can occur in any of the pathways (since the au kinda has Good, Neutrall and Bad ones)

The changing plant can have different endings, depending on Stanley and Narrator's relationship beforehand (which is a thing for the whole AU and is kinda based on the count of specific endings before the fern gets put in the bucket and then after the fact) so here is a summary:

Best - Fernator asks Stanley if he would be a bit changed and gives Stanley ability to create flowers

Good - Fernator doesn't ask Stanley but listens when Stanley says he doesn't want this. This can either go with no changes happening or some already starting and either staying or slowly regressing.

Neutral - Fernator doesn't ask and listen to Stanley. Stanley with his free will makes it slow down and gets the abilites the form would give, he might or might not have to hurt Fernie to do that though.

Neutral 2 - Fernator doesn't ask and listen to Stanley at first but later realises that he is hurting the man and stops the process. This one depends if Fernie gave Stanley ability to feel or not

Bad - He doesn't ask or listen: - Stanley becomes a literal plant without ability to talk - Stanley's hatred gets him through and he becomes a meaty mass, spreadingacross the office like a tumour - Stanley doesn't get changed and instead just becomes a corpse

Some of these end up without a chance of reset unless you get a New Save

These are the basic endings for this, with small alternation that might happen between them as many endings can be similar in different pathways!!

Thank you so much anon for your ask and interest ;w;

[sugatangbear]

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Diagnosis could be the process of finding out if a patient has a specific disease. A medical professional prescribes a test to make a diagnosis or to exclude possible illness. The clinical course in the first case might be to implement appropriate treatment for the diagnosed disease, while in the second case other diagnostic tests have to be pursued.

For some diseases, it is not only important to know what the nature of the disease is but also the degree of development. Doctors may need to be aware of the stage of the disease, its progresses, whether it is stable or in regression. Check disclaimer on profile and landing page.

[queer-crusader]

Okay update on my life since it seems talking about it doesn’t trigger another panic attack/breakdown:

So i graduated in july right

And with the end of uni, my student funding ends too

So i look for a job bc i cannot sustain myself otherwise

Except the economy is shit, because the UK is handling the pandemic almost worse than any other country in the world (we love that)

Knowing i’ll need some financial support to tie me over, i apply to universal credit

I also know my roommate, who i’ve lived with for 5 years, is moving out in october, and i will need to find someone to replace her or i end up paying £1000/month for living in this flat, which i don’t have of course

Job search becomes more frantic and exhausting and stressful

Also my dad started throwing up at some point and is eating less and is very specific about not upsetting his stomach. This is strange because he is known for his iron stomach and has not thrown up in years. I know my family history, i have my suspicions, but the doctor says it could be an ulcer. It could be fine, but my brain jumps to the worst-case scenario, because why wouldn’t it? More stress

Universal credit gets back to me - application denied

I think, hey, the category they filed me under seems wrong, i should be a habitual resident, not an EEA jobseeker, because i’ve lived here 6 years now. So i apply for an appeal, explaining the situation

Few weeks later, i receive a letter. Appeal rejected. It goes into detail how some rule that was set up in 2016 (Brexit year) lists all the reasons why just living here for 6 years, building up contacts, creating a future, feeling at home, being allowed to vote for Scottish parliament elections, is not good enough. Every sentence is like a punch in the gut. The letter boils down to fancy government words that translate to “you’re a freeloading immigrant who, according to our records, might as well be living in Fiji, and we’re giving you fuck all. Good luck surviving”

Full-blown breakdown ensues, because I’ve been fearing this ever since i arrived but was told by EVERYONE that that fear is ridiculous. I fit in, i belong, i sound English, i’m fluent, i’m passionate and well-educated about local politics, etc. I knew it wouldn’t be good enough. Race doesn’t matter; I’m European, and for the UK government, that’s good enough.

Anyway, cue the next day, and my mum phones me with news

My dad is in hospital. Turns out i was right - bowel cancer. He’s going into emergency surgery the very next day to get a tumour removed

I don’t sleep that night, for obvious reasons

Dad comes out of surgery fine, they got the whole thing, took some extra tests to see if it spread but it’s looking good so far. Meanwhile i have images of my dad, skinny as hell and with a tube up his nose seared into my brain

I fly home two days later to be with my family, who obviously need me

My dad is cleared of cancer, which is AWESOME, but we do learn that if the doctors had waited a couple days longer he could have had a perforated bowel. My mum is furious with the GP who underestimated the case

I get in touch with my landlady, saying “hey, this is my life right now, i am not in a position to search for a roommate replacement. Here’s the pics we took of the flat, can you look yourself? Also, if i don’t find a job by the end of the month, I may have to move out as well due to financial struggles, so keep in mind there’s a chance you’re going to have to look for two new tenants”

Landlady’s reply: “oh i can’t afford for the flat to be empty so i’m gonna sell it now”

So now i don’t even have an option of keeping the flat. I’ll have to move out, job or not. I can’t afford a new flat, and i can’t look for one bc a) pandemic and b) im in another country looking after my recovering dad (who is still losing weight btw, 15kg or 30-something lbs or 2.5 stone in a month, it’s horrible to see but at least he’s feeling a little better each day)

If i lose my flat, i may not be able to get a UK job. If i don’t get a UK job, chances are, i can not return to Scotland

6 years of living here, of building friendships, contacts and connections, skills for a career (which is also down the drain - theatre, an industry that is currently being killed by a lovely combo of the UK govt and the pandemic), a home, a love for the county, an intimate knowledge of the workings here, the language, the system, the stories, the history, i almost know the system here better than the Dutch one - my entire adult life. I may lose.

There is a chance i’ll be able to cling on, and god im fighting for it with the few spoons i have after all this stress, but the chance of me losing everything is equally plausible.

I have now flown back to Scotland where I put myself in self-isolation

In a week, my roommate will have moved out and i have 10 or so days left stuck in this place all by myself

I will spend this time packing up all my belongings, choosing what to take back to my parents’ place with me and what to put into storage, which i will pay for with my remaining savings and some financial support from the parents (they can’t afford much tho, my mum is unemployed and on benefits and my dad is a freelancer recovering from fucking surgery. I have no idea what their financial situation is right now, but apparently they’re okay-ish with their savings. Still, stress, and i don’t wanna burden them even more)

Then there’s the hope that the lockdown won’t have regressed back to that point where every plane is cancelled, and i’m stuck in this country without a place to call my home. (Don’t worry, i won’t end up on the street if this happens, I have friends willing to shelter me until i can fly home if they have to)

And once i’ve left, it’s only a question of when, and more promenently if, I’ll be able to return here, to Scotland.

I have never been this stressed, and i have never been this terrified. I am angry all the time (yes you can read that in Zuko’s voice lmao), I’m exhausted, and i’m fuelled by spite against prime minister Blow-Job and sheer stubbornness in refusing to feel like shit, because i just can’t be bothered with that. I just about manage to get through the day, to get up at a reasonable time, to feed myself, to shower, to exercise (because if i don’t, my wonky hip will give me hell and i’ll be in agony on top of my depression and anxiety. We love functioning bodies). But I’ll be okay. I’m trying to find solutions for everything, one step at a time. I’m taking care of myself the best i can. And if you wonder where my writing updates are, or my shitposts, or my ridiculously excited tags, then firstly, thank you for noticing ohmygod i love you, and secondly, know that i’ll be back. If God exists, know im kicking their ass. Fuck all this bullshit, my life is a mess but i REFUSE to let it stop me in my tracks. I’m too powerful, i am Brian David Gilbert’s interpretation of Sonic (either a god or can kill god and it doesn’t matter which). I’m gonna keep on truckin.